This section is intended to introduce various aspects of the art, which may be associated with embodiments of the disclosed techniques and methodologies. A list of references is provided at the end of this section and may be referred to hereinafter. This discussion, including the references, is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the disclosure. Accordingly, this section should be read in this light and not necessarily as admissions of prior art.
Reservoir surveillance during hydrocarbon reservoir production is a key to meeting goals of reduced operating costs and maximized recovery. Differences between actual and predicted performance are typically used to update the reservoir's geological model and to revise its depletion strategy. The changes in reservoir fluid saturation, pressure and temperature that occur during production also induce changes in the reservoir acoustic properties of rocks that under favorable conditions may be detected by seismic methods. One way to observe changes in properties in a hydrocarbon reservoir is to compare seismic returns measured at different times. The resultant gathered data is known as 4D seismic data.
For porous siliciclastic sands, seismic response is generally strong enough to detect fluid movement (Johnston et al., many others). Using different forms of approximations of the reflectivity equation and a rock physics model, a set of coefficients can be estimated to make a combination of near and far difference amplitudes, which in turn may infer the saturation and pressure change (Tura, et al, 1998; Landro, 2001; Lumley, et al, 2003, Ribeiro & MacBeth, 2004, Angelov et al, 2004). This type of formulation works well when physical properties of the reservoir, such as porosity (phi) and volume of shale (vsh), are relatively constant.
Seismic velocities change as hydrocarbon saturation change caused by production. The amount of the velocity change for a given saturation change depends on a number of factors, i.e. nature of the rock frame; fluid property; pore pressure, temperature of the reservoir, etc. These dependencies are well understood and are implemented in currently available commercial software for fluid substitutions to model/explain the fluid effect on seismic data. The scales at which the two fluids (e.g. oil and water) are mixed have an impact on the velocity change from experiment data (Knight and Nolen-Hoeksema, 1990, Endres and Knight, 1989) and theoretical derivations (Biot 1956, Mavko and Nur 1975, Stoll, 1989, Dvorkin and Nur, 1993). The mixing pattern of the two fluids is in turn influenced by the permeability at a given frequency (e.g. seismic frequency) (Batzle et al. 2006). For low permeability rocks, fluids can not move easily as in the higher permeability rock. Fluid moving through low permeability rock therefore has a higher chance of showing unpredictable or patchy behavior at frequency ranges associated with seismic surveys.
Time shift is the difference in two-way seismic travel times that are observed when analyzing seismic surveys conducted at different times. Time shifts can be attributed to two sources: 1) pore-fluid property changes that alter the velocity at which seismic signals pass through a layer or interval, and 2) changes in seismic velocity and layer thickness that occur both inside and outside of the layer or overall reservoir because of reservoir compaction and stress-strain redistribution in the surrounding formations. For a layer with thickness, z, the change in relative seismic travel time is (Landro and Stammeijer, 2004)Δt/t=Δz/z−Δv/v  (1)where t represents the two-way travel time across the layer and v is the velocity of the layer. Hatchell and Bourne (2005) has used time shifts to estimate the compaction of compacting reservoirs and layers within reservoirs by implicitly assuming the velocity change due to fluid is a minor component. However, compaction (as expressed by Δz) does not always contribute significantly to the time shifts, especially when reservoir rock is stiff or pressure is well maintained. In this case, the main contributing factor to an observed time shift will be the velocity change from either fluid change (such as saturation levels) or pressure change.
Separating the contributions from pressure change or saturation change is important to an effective 4D seismic model. He et al. (1998) has proposed to invert the 4D seismic data to impedance volumes and from there pressure and saturation change are inferred. Chu and Grant (2007) proposed a method to invert saturation and pressure directly from 4D seismic data by integrating well information, reserve simulation and 4D seismic data. However, when 4D signals are weak, such as in hard rock formations, a more model based methodology is desired.
An additional concern is that when a reservoir is thick, it may span a few cycles of seismic wave events, so that side-lobe energy may generate apparent difference events that can appear as real reservoir differences. This side-lobe energy complicates interpretation of multi-cycle hydrocarbon reservoirs where there is interference between reflectors. One proposed method to overcome this is to invert saturation and pressures changes at well locations using rock physics models and then populate the saturation and pressure changes in-between wells by statistical calibration methods. However, in hard rocks, such as carbonate rocks, seismic signals may not provide an adequate signal return to observe changes in hydrocarbon reservoir property values over time. What is needed is a method to optimally evaluate dynamic reservoir properties, such as saturation changes and pressure changes, under weak 4D signal scenarios generally encountered when dealing with hard rock formations.
The foregoing discussion of need in the art is intended to be representative rather than exhaustive. A technology addressing one or more such needs, or some other related shortcoming in the field, would benefit drilling and reservoir development planning, for example, providing decisions or plans for developing a reservoir more effectively and more profitably.
Reference material which may be relevant to the invention, and which may be referred to herein, include Angelov, P. et al, “Pore Pressure and Water Saturation Variations—Modification of Landro's AVO Approach,” Expanded Abstracts: 74th Annual Meeting of the SEG (2004); Hatchell, P. J. et al., Measuring Reservoir Compaction Using Time-Lapse Timeshifts,” Expanded Abstracts: 75th Annual Meeting of the SEG (2005); Landro, M., “Discrimination Between Pressure and Fluid Saturation Changes From Time-Lapse Seismic Data,” Geophysics, 66, 836-844 (2001); Lumley, D. E. et al., “Estimation of Reservoir Pressure and Saturations by Crossplot Inversion of 4D Seismic (2003); Ribeiro, C. et al., “A Petroelastic-Based Approach to Pressure and Saturation Estimation Using 4D Seismic,” Expanded Abstracts: 74th Annual Meeting of the SEG (2004); Tura, A. et al., “Subsurface Fluid Flow Properties From Time-Lapse Elastic Wave Reflection Data,” Proceedings of SPIE, Mathematical Methods in Geophysical Imaging V, V. 3453, 125-138 (1998); Knight, R. et al., “A Laboratory Study of the Dependence of Elastic Wave Velocities on Pore Scale Fluid Distribution,” Geopys. Res. Lett., 14, 1529-1532 (1990); Endres, A. L. et al., “A Theoretical Treatment of the Effect of Microscopic Fluid Distribution on the Dielectric Properties of Partially Saturated Rocks,” Geophys. Prospecting, 40, 307-324 (1992); Biot, M. A., “Theory of Propagation of Elastic Waves in Fluid Saturated Porous Solid, I. Low Frequency Range and II. Higher-Frequency Range,” J. Acoust. Soc. Am., 28, 168-191 (1956); Mavko, G. et al., “Melt Squirt in the Asthenosphere,” J. Geophys. Res., 80, 1444-1448 (1975); Stoll, R. D. “Sediment Acoustics,” Springer Verlag, Berlin, 154, (1989); Dvorkin, J. et al., “Dynamic Poroelasticity: A Unified Model With the Squirt and the Biot Mechanisms,” Geophys., 58, 524-533, (1993); Batzle, et al., “Fluid Mobility and Frequency-Dependent Seismic Velocity—Direct Measurements,” Geophysics, 71, N1-N9, (2006); Landro, M. et al., “Quantitative Estimation of Compaction of Velocity Changes Using 4D Impedance and Traveltime Changes,” Geophysics, 69, 949-957, (2004); U.S. Pat. No. 5,798,982; PCT Patent Application No. PCT/US08/03830, Inversion of 4D Seismic Data, with inventors Chu, D. et al.